





'''simple docstring'''

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:     List[str]			=							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						AutoencoderKL			,					lowerCAmelCase__  :						CLIPTextModel			,					lowerCAmelCase__  :						CLIPTokenizer			,					lowerCAmelCase__  :						UNetaDConditionModel			,					lowerCAmelCase__  :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,					lowerCAmelCase__  :						StableDiffusionSafetyChecker			,					lowerCAmelCase__  :						CLIPImageProcessor			,					)       ->	Optional[Any]:

            '''simple docstring'''




            super().__init__()
            self.register_modules(
                vae=lowerCAmelCase__			,					text_encoder=lowerCAmelCase__			,					tokenizer=lowerCAmelCase__			,					unet=lowerCAmelCase__			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Union[str, int]] = "auto"	)       ->	Union[str, Any]:

            '''simple docstring'''




            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 _UpperCamelCase							       =			self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            self.enable_attention_slicing(lowerCAmelCase__	)







       @torch.no_grad()
       def __call__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Union[str, List[str]]			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 50			,					lowerCAmelCase__  :						float = 7.5			,					lowerCAmelCase__  :						Optional[Union[str, List[str]]] = None			,					lowerCAmelCase__  :						Optional[int] = 1			,					lowerCAmelCase__  :						float = 0.0			,					lowerCAmelCase__  :						Optional[torch.Generator] = None			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					lowerCAmelCase__  :						Optional[str] = "pil"			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,					lowerCAmelCase__  :						int = 1			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					**lowerCAmelCase__  :						Optional[int]			,					)       ->	Any:

            '''simple docstring'''




            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			1
            elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			len(lowerCAmelCase__	)
            else:
                 raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__	)}"""	)

            if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

            if (callback_steps is None) or (
                callback_steps is not None and (not isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) or callback_steps <= 0)
            ):
                 raise ValueError(
                     f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                     f""" {type(lowerCAmelCase__	)}."""	)

            # get prompt text embeddings
            _UpperCamelCase							       =			self.tokenizer(
                lowerCAmelCase__			,					padding='''max_length'''			,					max_length=self.tokenizer.model_max_length			,					return_tensors='''pt'''			,					)
            _UpperCamelCase							       =			text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 _UpperCamelCase							       =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
                 logger.warning(
                     '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                     f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""	)
                 _UpperCamelCase							       =			text_input_ids[:, : self.tokenizer.model_max_length]

            if text_embeddings is None:
                 _UpperCamelCase							       =			self.text_encoder(text_input_ids.to(self.device	)	)[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			text_embeddings.shape
            _UpperCamelCase							       =			text_embeddings.repeat(1			,					lowerCAmelCase__			,					1	)
            _UpperCamelCase							       =			text_embeddings.view(bs_embed * num_images_per_prompt			,					lowerCAmelCase__			,					-1	)

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            _UpperCamelCase							       =			guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 _UpperCamelCase							       =			42
                 if negative_prompt is None:
                      _UpperCamelCase							       =			['''''']
                 elif type(lowerCAmelCase__	) is not type(lowerCAmelCase__	):
                      raise TypeError(
                          f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__	)} !="""
                          f""" {type(lowerCAmelCase__	)}."""	)
                 elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                      _UpperCamelCase							       =			[negative_prompt]
                 elif batch_size != len(lowerCAmelCase__	):
                      raise ValueError(
                          f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__	)}, but `prompt`:"""
                          f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                          ''' the batch size of `prompt`.'''	)
                 else:
                      _UpperCamelCase							       =			negative_prompt

                 _UpperCamelCase							       =			text_input_ids.shape[-1]
                 _UpperCamelCase							       =			self.tokenizer(
                     lowerCAmelCase__			,					padding='''max_length'''			,					max_length=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					return_tensors='''pt'''			,					)
                 _UpperCamelCase							       =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _UpperCamelCase							       =			uncond_embeddings.shape[1]
                 _UpperCamelCase							       =			uncond_embeddings.repeat(lowerCAmelCase__			,					lowerCAmelCase__			,					1	)
                 _UpperCamelCase							       =			uncond_embeddings.view(batch_size * num_images_per_prompt			,					lowerCAmelCase__			,					-1	)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 _UpperCamelCase							       =			torch.cat([uncond_embeddings, text_embeddings]	)

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
            _UpperCamelCase							       =			text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(self.device	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(
                          self.device	)
                 else:
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
            else:
                 if latents_reference.shape != latents_shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
                 _UpperCamelCase							       =			latents_reference.to(self.device	)
                 _UpperCamelCase							       =			latents.to(self.device	)

            # This is the key part of the pipeline where we
            # try to ensure that the generated images w/ the same seed
            # but different sizes actually result in similar images
            _UpperCamelCase							       =			(latents_shape[3] - latents_shape_reference[3]) // 2
            _UpperCamelCase							       =			(latents_shape[2] - latents_shape_reference[2]) // 2
            _UpperCamelCase							       =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
            _UpperCamelCase							       =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
            _UpperCamelCase							       =			0 if dx < 0 else dx
            _UpperCamelCase							       =			0 if dy < 0 else dy
            _UpperCamelCase							       =			max(-dx			,					0	)
            _UpperCamelCase							       =			max(-dy			,					0	)
            # import pdb
            # pdb.set_trace()
            _UpperCamelCase							       =			latents_reference[:, :, dy : dy + h, dx : dx + w]

            # set timesteps
            self.scheduler.set_timesteps(lowerCAmelCase__	)

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            _UpperCamelCase							       =			self.scheduler.timesteps.to(self.device	)

            # scale the initial noise by the standard deviation required by the scheduler
            _UpperCamelCase							       =			latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _UpperCamelCase							       =			'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
            _UpperCamelCase							       =			{}
            if accepts_eta:
                 _UpperCamelCase							       =			eta

            for i, t in enumerate(self.progress_bar(lowerCAmelCase__	)	):
                 # expand the latents if we are doing classifier free guidance
                 _UpperCamelCase							       =			torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
                 _UpperCamelCase							       =			self.scheduler.scale_model_input(lowerCAmelCase__			,					lowerCAmelCase__	)

                 # predict the noise residual
                 _UpperCamelCase							       =			self.unet(lowerCAmelCase__			,					lowerCAmelCase__			,					encoder_hidden_states=lowerCAmelCase__	).sample

                 # perform guidance
                 if do_classifier_free_guidance:
                      _UpperCamelCase       ,							_UpperCamelCase							       =			noise_pred.chunk(2	)
                      _UpperCamelCase							       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCamelCase							       =			self.scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					**lowerCAmelCase__	).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                      callback(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			1 / 0.18215 * latents
            _UpperCamelCase							       =			self.vae.decode(lowerCAmelCase__	).sample

            _UpperCamelCase							       =			(image / 2 + 0.5).clamp(0			,					1	)

            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            _UpperCamelCase							       =			image.cpu().permute(0			,					2			,					3			,					1	).float().numpy()

            if self.safety_checker is not None:
                 _UpperCamelCase							       =			self.feature_extractor(self.numpy_to_pil(lowerCAmelCase__	)			,					return_tensors='''pt'''	).to(
                     self.device	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			self.safety_checker(
                     images=lowerCAmelCase__			,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
            else:
                 _UpperCamelCase							       =			None

            if output_type == "pil":
                 _UpperCamelCase							       =			self.numpy_to_pil(lowerCAmelCase__	)

            if not return_dict:
                 return (image, has_nsfw_concept)

            return StableDiffusionPipelineOutput(images=lowerCAmelCase__			,					nsfw_content_detected=lowerCAmelCase__	)









"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase				:     Any					=				logging.get_logger(__name__)

lowerCAmelCase				:     int					=				"""▁"""

lowerCAmelCase				:     str					=				{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase				:     Union[str, Any]					=				{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

lowerCAmelCase				:     Any					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

lowerCAmelCase				:     List[Any]					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}



def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   collections.OrderedDict()
			with open(snake_case__			,					"""r"""			,					encoding="""utf-8"""							) as reader:
						lowerCamelCase      =   reader.readlines()
			for index, token in enumerate(snake_case__							):
						lowerCamelCase      =   token.rstrip("""\n"""							)
						lowerCamelCase      =   index
			return vocab






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		VOCAB_FILES_NAMES
				__UpperCamelCase         =		PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =		["input_ids", "attention_mask"]





				def __init__(			self						,					_a						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[UNK]"						,					_a="[PAD]"						,					_a="[CLS]"						,					_a="[MASK]"						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_a						,					eos_token=_a						,					sep_token=_a						,					unk_token=_a						,					pad_token=_a						,					cls_token=_a						,					mask_token=_a						,					sp_model_kwargs=self.sp_model_kwargs						,					**_a						,					)

							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(_a					)					)
							lowerCamelCase      =   vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							lowerCamelCase      =   {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

							for i in range(10					):
										lowerCamelCase      =   f'[unused{i}]'
										lowerCamelCase      =   5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							lowerCamelCase      =   12
							lowerCamelCase      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(_a					)





				def __getstate__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.__dict__.copy()
							lowerCamelCase      =   None
							return state





				def __setstate__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   d
							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							# for backward compatibility
							if not hasattr(self						,					"""sp_model_kwargs"""					):
										lowerCamelCase      =   {}

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = False					):





							"""simple docstring"""

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_a						,					token_ids_a=_a						,					already_has_special_tokens=_a					)

							if token_ids_a is None:
										return ([0] * len(_a					)) + [1]
							return ([0] * len(_a					)) + [1] + ([0] * len(_a					)) + [1]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							lowerCamelCase      =   [self.sep_token_id]

							if token_ids_a is None:
										return len(token_ids_a + sep					) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return len(self.sp_model					) + self.fairseq_offset





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.sp_model.encode(_a						,					out_type=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCamelCase      =   self.sp_model.PieceToId(_a					)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   """""".join(_a					).replace(_a						,					""" """					).strip()
							return out_string





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							if not os.path.isdir(_a					):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
										return
							lowerCamelCase      =   os.path.join(
							    _a						,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(_a					) and os.path.isfile(self.vocab_file					):
										copyfile(self.vocab_file						,					_a					)
							elif not os.path.isfile(self.vocab_file					):
										with open(_a						,					"""wb"""					) as fi:
													lowerCamelCase      =   self.sp_model.serialized_model_proto()
													fi.write(_a					)

							return (out_vocab_file,)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""

							if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
							lowerCamelCase      =   [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep



